A computer storage or memory comprises components used to retain digital data. The computer memory is a core component of computer systems. Computer systems generally incorporate a storage hierarchy. The traditional divisions of computer storage are primary, secondary, tertiary and off-line storage. Primary storage (or main memory) is often referred to as the memory. The primary memory is the only memory type that is directly accessible to the central processing unit (CPU). The CPU continuously reads instructions stored there and executes them as required. Any data actively operated on is also stored there in uniform manner. Secondary storage (also known as external memory or auxiliary storage), differs from primary storage in that it is not directly accessible by the CPU. The computer usually uses its input/output channels to access secondary storage and transfers the desired data using intermediate area in primary storage. Tertiary storage or tertiary memory provides a third level of storage. Tertiary storage involves a robotic device which mounts, inserts and dismounts removable mass storage media into a storage device according to the system's demands. This data is often copied to secondary storage before use. It is primarily used for archiving rarely accessed information since it is much slower than secondary storage. Tertiary storage is primarily useful for extraordinarily large data stores, accessed without human operators. Off-line storage is a computer data storage on a medium or a device that is not under the control of a processing unit. The medium is recorded, usually in a secondary or tertiary storage device, and then physically removed or disconnected. It must be inserted or connected by a human operator before a computer can access it again. Unlike tertiary storage, it cannot be accessed without human interaction. Off-line storage is used to transfer information, since the detached medium can be easily physically transported.
As technology has progressed, another form of computer storage is increasing in popularity and usage. This form of storage is referred to as “cloud storage”. Cloud storage is based on cloud computing. Cloud computing describes a variety of computing concepts that involve a large number of computers connected through a real-time communication networks such as the Internet. In science, cloud computing is a synonym for distributed computing over a network, and means the ability to run a program or application on many connected computers at the same time.
Cloud Computing Architecture
A cloud computing system is generally divided into two sections: the front end and the back end. These sections connect to each other through a communication network such as the Internet. The user or client communicates with the system through the front end section. The back end section is the “cloud” section of the system.
The front end includes the client's machine and the application required to access the cloud computing system. Not all cloud computing systems have the same user interface. Services like Web-based e-mail programs leverage existing Web browsers like Internet Explorer or Firefox. Other systems have unique applications that provide network access to clients. On the back end of the system are the various computers, servers and data storage systems that create the “cloud” of computing services. In theory, a cloud computing system could include practically any computer program you can imagine, from data processing to video games. Usually, each application will have its own dedicated server.
In a cloud computing system, there's a significant workload shift. Local computers no longer have to do all the heavy lifting when it comes to running applications. The network of computers that make up the cloud handles them instead. Hardware and software demands on the user's side decrease. The only requirement is that the user's computer must execute the cloud computing system's interface software. This interface software can be a basic Web browser. The cloud network covers the rest of the operations.
As cloud computing has become a strategic initiative for large enterprises, the new method of delivering and consuming IT services has forced its users to rethink activities such as job scheduling. One aspect of job scheduling in cloud technology is workforce automation. Workload as use herein is an abstraction of a process or ser of processes that can be componentized, individually operated upon and produce a determinate result, with the abstraction being above the network hardware and operating system layers. A job scheduler is a tool that allows management and scheduling of jobs or workloads using a calendar system.
Workload automation is the evolution of job scheduling with advanced workload management capabilities for the dynamic data center. The aspects of scheduling workloads include automatically resolving complex dependencies on various platforms and application tiers and then triggering workloads based on both IT and business events.
A primary function of a good workload automation solution is to provide visibility into enterprise-wide workloads, regardless of where the workload or the workload automation solution is physically located. However, workloads are not operated along platform lines of separation. They have cross-platform dependences for computing needs and for application dependences. For instance, the workload automation solution could be on a mainframe but the workloads could be running on distributed platforms, or vice-versa. Most vendors have separate solutions for each platform, making it difficult for IT operations to understand workload dependencies across platforms or virtual servers.
For a dynamic workload automation solution, it becomes even more complex when workloads are run in the cloud, another virtual resource. This makes it important for the workload automation solution to be able to offer full flexibility in its ability to operate agents across platforms, virtual resource and the cloud and visibility into all of these workloads from a single place. To cite an example, CA Workload Automation solution's CA Workload Command Center displays visibility into workloads in mainframe, distributed and Amazon EC2 cloud—all in a single pane. This gives workload administrators visibility into enterprise-wide workload infrastructure.
The second aspect of cross-platform workload management, beyond visibility as discussed above is control. Workload administrators need the ability to apply job definitions that abstract out the platform differences sufficiently in order to avoid recreating multiple job definitions for each platform. This saves time, not only for adding new job definitions, but also on maintenance and service and helps IT operations be more responsive to business needs.
Users access cloud computing using networked client devices, such as desktop computers, laptops, tablets and smart phones. Cloud configurations can take the form of public clouds, private clouds or hybrid clouds. Private cloud is cloud infrastructure operated solely for a single organization, whether managed internally or by a third-party and hosted internally or externally. Undertaking a private cloud project requires a significant level and degree of engagement to virtualize the business environment, and requires the organization to reevaluate decisions about existing resources. When done right, it can improve business, but every step in the project raises security issues that must be addressed to prevent serious vulnerabilities. They have attracted criticism because users “still have to buy, build, and manage them” and thus do not benefit from less hands-on management, essentially “[lacking] the economic model that makes cloud computing such an intriguing concept”.
A cloud is a “public cloud” when the services are rendered over a network that is open for public use. There is little difference between the architecture of a public and a private cloud. However, security considerations can be substantially different for services (applications, storage, and other resources) that are made available by a service provider. Generally, public cloud service providers like Amazon AWS, Microsoft and Google own and operate the infrastructure and offer access only via Internet (direct connectivity is not offered).
A hybrid cloud consists of private cloud and public cloud components. In a hybrid cloud, there has to be a determination of which component (public or private) will run a virtualized workload? For example, when assigning a virtualized server, one may want to assign it to the least expensive option, whether that be public or private. In the alternative, they may want to assign the virtual server to the private cloud until there are no more resources available, then assign virtual servers to the public cloud. In addition, a newly requested virtual server may have a higher priority for the private cloud and “bump” existing virtual servers to the public cloud http://www.globalstf.org/docs/proceedings/ccv/135.pdf discusses A Decision Support System for Moving Workloads to Public Clouds. This is different from our idea because it talks more about a decision to migrate existing bare metal applications to a virtual environment.
A central server administers the system, monitoring traffic and client demands to ensure everything runs smoothly. Most of the time, servers don't run at full capacity. That means there's unused processing power going to waste. There is a need for a method and system for migrating workloads between public clouds and between public and private clouds. Further, there is a need to consider provisioning virtual machines on demand to meet new requirements and accounts for the possibility of choosing dynamically from several different cloud environments to take advantage of the best fit.